ArticleJournal of cheminformatics2022
PIKAChU: a Python-based informatics kit for analysing chemical units.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- PARAS: High-Accuracy Machine Learning of Substrate Specificities in Nonribosomal Peptide Synthetases.JACS Au · 2026Article
- Machine learning: Python tools for studying biomolecules and drug design.Molecular diversity · 2025Review
- Python tools for structural tasks in chemistry.Molecular diversity · 2025Review
- Optimisation of surfactin yield inComputational and structural biotechnology journal · 2024Article
- RAIChU: automating the visualisation of natural product biosynthesis.Journal of cheminformatics · 2024Article
- Cheminformatics Microservice: unifying access to open cheminformatics toolkits.Journal of cheminformatics · 2023Article
- DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications.Nature communications · 2023Article
- MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters.Nucleic acids research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
As efforts to computationally describe and simulate the biochemical world become more commonplace, computer programs that are capable of in silico chemistry play an increasingly important role in biochemical research. While such programs exist, they are often dependency-heavy, difficult to navigate, or not written in Python, the programming language of choice for bioinformaticians. Here, we introduce PIKAChU (Python-based Informatics Kit for Analysing CHemical Units): a cheminformatics toolbox with few dependencies implemented in Python. PIKAChU builds comprehensive molecular graphs from SMILES strings, which allow for easy downstream analysis and visualisation of molecules. While the molecular graphs PIKAChU generates are extensive, storing and inferring information on aromaticity, chirality, charge, hybridisation and electron orbitals, PIKAChU limits itself to applications that will be sufficient for most casual users and downstream Python-based tools and databases, such as Morgan fingerprinting, similarity scoring, substructure matching and customisable visualisation. In addition, it comes with a set of functions that assists in the easy implementation of reaction mechanisms. Its minimalistic design makes PIKAChU straightforward to use and install, in stark contrast to many existing toolkits, which are more difficult to navigate and come with a plethora of dependencies that may cause compatibility issues with downstream tools. As such, PIKAChU provides an alternative for researchers for whom basic cheminformatic processing suffices, and can be easily integrated into downstream bioinformatics and cheminformatics tools. PIKAChU is available at https://github.com/BTheDragonMaster/pikachu .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.